Improving design in the context of market systems requires an understanding of how consumers learn about and evaluate competing products. Marketing models frequently assume that consumers choose the product with the highest utility, which provides businesses insights into how to design and price their products to maximize profits. While recent research has shown the impacts of consumer interactions within social networks on their purchasing decisions, they typically model market systems using a top-down approach. This paper applies an agent-based modeling approach with social network models to investigate the extent to which word-of-mouth (WOM) communications are influential in changing consumer preferences and producer market performance. Using a random network, we study the effects of the number of referrals for a product and the degrees of similarity between the senders and receivers of referrals on purchase decisions. In addition, the eigenvector centrality metric is used to analyze the spread of WOM referrals. The simulation results show that the most influential consumers in the network can create significant shifts in the market share, and a statistical analysis reveals a significant change in the system-level metrics of interest for the competing firms when WOM recommendations are included. The findings incentivize producers to invest in supporting their product development efforts with rigorous social networks analysis so as to increase their market success.
{"title":"Word-of-Mouth Recommendations in an Automobile Market System","authors":"Amineh Zadbood, Nicholas Russo, Steven Hoffenson","doi":"10.1115/detc2019-97680","DOIUrl":"https://doi.org/10.1115/detc2019-97680","url":null,"abstract":"\u0000 Improving design in the context of market systems requires an understanding of how consumers learn about and evaluate competing products. Marketing models frequently assume that consumers choose the product with the highest utility, which provides businesses insights into how to design and price their products to maximize profits. While recent research has shown the impacts of consumer interactions within social networks on their purchasing decisions, they typically model market systems using a top-down approach. This paper applies an agent-based modeling approach with social network models to investigate the extent to which word-of-mouth (WOM) communications are influential in changing consumer preferences and producer market performance. Using a random network, we study the effects of the number of referrals for a product and the degrees of similarity between the senders and receivers of referrals on purchase decisions. In addition, the eigenvector centrality metric is used to analyze the spread of WOM referrals. The simulation results show that the most influential consumers in the network can create significant shifts in the market share, and a statistical analysis reveals a significant change in the system-level metrics of interest for the competing firms when WOM recommendations are included. The findings incentivize producers to invest in supporting their product development efforts with rigorous social networks analysis so as to increase their market success.","PeriodicalId":365601,"journal":{"name":"Volume 2A: 45th Design Automation Conference","volume":" 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113951777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rizwan Pathan, Soban Babu Beemaraj, A. Salvi, Gehendra Sharma, J. Allen, F. Mistree
Composite materials are increasingly being used in load bearing structures due to their high specific stiffness and strength. Designing composite structures involve solving multiple conflicting objectives (e.g weight and deflection) and constraints (e.g failure stress and strain), which is a challenging task. In the absence of an optimal solution, a compromise solution is desired. Concurrent (material selection plus sizing) design approach using Decision Support Problem (DSP) is used to arrive at a compromise solution. In this paper multiscale design approach is proposed, that incorporates the tailoring of material microstructures and sizing to achieve improved compromise solution. The microstructure properties are obtained by using analytical and computational models for various composite materials. These models compute structure-property relations between bulk material properties and their micro-structural constituents. The approach is demonstrated with an example of a sandwich composite cantilever beam subjected to multiple load cases. An efficiency factor (η) is defined to compare the results of concurrent design approach and multiscale design approach.
{"title":"Design of Composite Structures Through Decision Support Problem and Multiscale Design Approach","authors":"Rizwan Pathan, Soban Babu Beemaraj, A. Salvi, Gehendra Sharma, J. Allen, F. Mistree","doi":"10.1115/detc2019-97894","DOIUrl":"https://doi.org/10.1115/detc2019-97894","url":null,"abstract":"\u0000 Composite materials are increasingly being used in load bearing structures due to their high specific stiffness and strength. Designing composite structures involve solving multiple conflicting objectives (e.g weight and deflection) and constraints (e.g failure stress and strain), which is a challenging task. In the absence of an optimal solution, a compromise solution is desired. Concurrent (material selection plus sizing) design approach using Decision Support Problem (DSP) is used to arrive at a compromise solution. In this paper multiscale design approach is proposed, that incorporates the tailoring of material microstructures and sizing to achieve improved compromise solution. The microstructure properties are obtained by using analytical and computational models for various composite materials. These models compute structure-property relations between bulk material properties and their micro-structural constituents. The approach is demonstrated with an example of a sandwich composite cantilever beam subjected to multiple load cases. An efficiency factor (η) is defined to compare the results of concurrent design approach and multiscale design approach.","PeriodicalId":365601,"journal":{"name":"Volume 2A: 45th Design Automation Conference","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121404195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we present a method for multi-material topology optimization of lattice structures for maximum bulk modulus. Unlike ground structure approaches that employ 1-d finite elements such as bars and beams to design periodic lattices, we employ a 3-d representation where each lattice bar is described as a cylinder. To accommodate the 3-d bars, we employ the geometry projection method, whereby a high-level parametric description of the bars is smoothly mapped onto a density field over a fixed analysis grid. In addition to the geometric parameters, we assign a size variable per material to each bar. By imposing suitable constraints in the optimization, we ensure that each bar is either made exclusively of one of a set of a multiple available materials or completely removed from the design. These optimization constraints, together with the material interpolation used in our formulation, make it easy to consider any number of available materials. Another advantage of our method over ground structure approaches with 1-d elements is that the bars in our method need not be connected at all times (i.e., they can ‘float’ within the design region), which makes it easier to find good designs with relatively few design variables. We illustrate the effectiveness of our method with numerical examples of bulk modulus maximization for two-material lattices with orthotropic symmetry, and for two- and three-material lattices with cubic symmetry.
{"title":"Topology Optimization of Multi-Material Lattices for Maximal Bulk Modulus","authors":"Hesaneh Kazemi, A. Vaziri, J. Norato","doi":"10.1115/detc2019-97370","DOIUrl":"https://doi.org/10.1115/detc2019-97370","url":null,"abstract":"\u0000 In this paper, we present a method for multi-material topology optimization of lattice structures for maximum bulk modulus. Unlike ground structure approaches that employ 1-d finite elements such as bars and beams to design periodic lattices, we employ a 3-d representation where each lattice bar is described as a cylinder. To accommodate the 3-d bars, we employ the geometry projection method, whereby a high-level parametric description of the bars is smoothly mapped onto a density field over a fixed analysis grid. In addition to the geometric parameters, we assign a size variable per material to each bar. By imposing suitable constraints in the optimization, we ensure that each bar is either made exclusively of one of a set of a multiple available materials or completely removed from the design. These optimization constraints, together with the material interpolation used in our formulation, make it easy to consider any number of available materials. Another advantage of our method over ground structure approaches with 1-d elements is that the bars in our method need not be connected at all times (i.e., they can ‘float’ within the design region), which makes it easier to find good designs with relatively few design variables. We illustrate the effectiveness of our method with numerical examples of bulk modulus maximization for two-material lattices with orthotropic symmetry, and for two- and three-material lattices with cubic symmetry.","PeriodicalId":365601,"journal":{"name":"Volume 2A: 45th Design Automation Conference","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121565087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The introduction of Additive Manufacturing opens up possibilities for creating lighter, better and customized products. However, to take advantage of the possibilities of Additive Manufacturing, the design engineer is challenged. In this paper, a general design process for the creation of complex products is proposed and evaluated. The proposed method aims to aid a design process in which Topology Optimization (TO) is used for concept development, and the result is then interpreted into a Master Model (MM) supporting design evaluations during detailed design. At the same time as the MM is created, information regarding manufacturing is saved in a database. This makes it possible to automatically generate and export models for manufacturing or CAE analyses. A tool that uses Knowledge-Based Engineering (KBE) to realize the presented methodology has been developed. The tool is specialized for the creation of structural components that connect to other components in an assembly. A case study, part of an aircraft door, has been used for evaluation of the tool. The study shows that the repetitive work when interpreting the topology-optimized design could be reduced. The result comes in the form of a parametric CAD model which allows fast changes and the coupled database enables the export of models for various purposes.
{"title":"Design for Additive Manufacturing Using a Master Model Approach","authors":"A. Wiberg, J. Andersson","doi":"10.1115/detc2019-97915","DOIUrl":"https://doi.org/10.1115/detc2019-97915","url":null,"abstract":"\u0000 The introduction of Additive Manufacturing opens up possibilities for creating lighter, better and customized products. However, to take advantage of the possibilities of Additive Manufacturing, the design engineer is challenged. In this paper, a general design process for the creation of complex products is proposed and evaluated. The proposed method aims to aid a design process in which Topology Optimization (TO) is used for concept development, and the result is then interpreted into a Master Model (MM) supporting design evaluations during detailed design. At the same time as the MM is created, information regarding manufacturing is saved in a database. This makes it possible to automatically generate and export models for manufacturing or CAE analyses. A tool that uses Knowledge-Based Engineering (KBE) to realize the presented methodology has been developed. The tool is specialized for the creation of structural components that connect to other components in an assembly. A case study, part of an aircraft door, has been used for evaluation of the tool. The study shows that the repetitive work when interpreting the topology-optimized design could be reduced. The result comes in the form of a parametric CAD model which allows fast changes and the coupled database enables the export of models for various purposes.","PeriodicalId":365601,"journal":{"name":"Volume 2A: 45th Design Automation Conference","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115203285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Reza Behrou, Reza Lotfi, J. Carstensen, James K. Guest
This paper presents an adaptive nodal boundary condition scheme to systematically enhance the computational efficiency and circumvent numerical instabilities of the finite element analysis in density-based topology optimization problems. The approach revisits the idea originally proposed by Bruns and Tortorelli to eliminate the contribution of void elements from the finite element model and extends this idea to modern projection methods to stabilize the implementation, facilitate reintroduction of material, and consider additional physics. The computational domain is discretized on a fixed finite element mesh and a threshold density is used to determine if an element is sufficiently low relative density to be “removed” from the finite element analysis. By eliminating low-density elements from the design domain, the number of free Degrees-Of-Freedom (DOFs) is reduced, thereby reducing the solution cost of the finite element equations. Perhaps more importantly, it circumvents numerical instabilities such as element distortion when considering large deformations. Unlike traditional solids-only modeling approaches, a key feature of the projection-based scheme is that the design and finite element spaces are separate, allowing the design variable sensitivities in a region to remain active (and potentially non-zero) even if the corresponding analysis elements are removed from the finite element model. This ultimately means material reintroduction is systematic and driven by the design sensitivities. The Solid Isotropic Material with Penalization (SIMP) approach is used to interpolate material properties and the Heaviside Projection Method (HPM) is used to regularize the optimization problem and facilitate material reintroduction through the gradient-based optimizer. Several benchmark examples in areas of linear and nonlinear structural mechanics are presented to demonstrate the performance of the proposed approach. The resulting optimized designs are consistent with literature and results reveal the performance and efficiency of the developed method in reducing computational costs without numerical instabilities known to be due to modeling near-void elements.
{"title":"An Adaptive and Efficient Boundary Approach for Density-Based Topology Optimization","authors":"Reza Behrou, Reza Lotfi, J. Carstensen, James K. Guest","doi":"10.1115/detc2019-98463","DOIUrl":"https://doi.org/10.1115/detc2019-98463","url":null,"abstract":"\u0000 This paper presents an adaptive nodal boundary condition scheme to systematically enhance the computational efficiency and circumvent numerical instabilities of the finite element analysis in density-based topology optimization problems. The approach revisits the idea originally proposed by Bruns and Tortorelli to eliminate the contribution of void elements from the finite element model and extends this idea to modern projection methods to stabilize the implementation, facilitate reintroduction of material, and consider additional physics. The computational domain is discretized on a fixed finite element mesh and a threshold density is used to determine if an element is sufficiently low relative density to be “removed” from the finite element analysis. By eliminating low-density elements from the design domain, the number of free Degrees-Of-Freedom (DOFs) is reduced, thereby reducing the solution cost of the finite element equations. Perhaps more importantly, it circumvents numerical instabilities such as element distortion when considering large deformations. Unlike traditional solids-only modeling approaches, a key feature of the projection-based scheme is that the design and finite element spaces are separate, allowing the design variable sensitivities in a region to remain active (and potentially non-zero) even if the corresponding analysis elements are removed from the finite element model. This ultimately means material reintroduction is systematic and driven by the design sensitivities. The Solid Isotropic Material with Penalization (SIMP) approach is used to interpolate material properties and the Heaviside Projection Method (HPM) is used to regularize the optimization problem and facilitate material reintroduction through the gradient-based optimizer. Several benchmark examples in areas of linear and nonlinear structural mechanics are presented to demonstrate the performance of the proposed approach. The resulting optimized designs are consistent with literature and results reveal the performance and efficiency of the developed method in reducing computational costs without numerical instabilities known to be due to modeling near-void elements.","PeriodicalId":365601,"journal":{"name":"Volume 2A: 45th Design Automation Conference","volume":"07 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128294772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Meta-materials are a class of artificial materials with a wide range of bulk properties that are different from the base material they are made of. The term meta-material in the context of this research refers to a continuous, heterogeneous structure with prescribed elastic properties. Such meta-materials are designed using Topology Optimization (TO). Tools like SIMP interpolation, mesh filtering and continuation methods are used to address the numerical issues with Topology Optimization. In a previous research [1], by offsetting meta-material layers by a half-width of the Unit Cell, an auxetic honeycomb-like geometry was obtained. This was the first time such a shape was observed as the result of Topology Optimization targeting the effective shear modulus using square Unit Cells. This was obtained while designing the shear beam of a non-pneumatic wheel. This research studies the design of meta-materials using offsets other than zero or half-widths. The same problem [1] was solved for different values of offset, and the obtained geometries and volume fractions are studied. It is concluded that it may be beneficial for designers to consider offsetting meta-material layers with offsets other than half-width, to design novel, potentially better performing structures.
{"title":"Multi-Scale Design of Meta-Materials With Offset Periodicity","authors":"R. Sadiwala, G. Fadel","doi":"10.1115/detc2019-98341","DOIUrl":"https://doi.org/10.1115/detc2019-98341","url":null,"abstract":"\u0000 Meta-materials are a class of artificial materials with a wide range of bulk properties that are different from the base material they are made of. The term meta-material in the context of this research refers to a continuous, heterogeneous structure with prescribed elastic properties. Such meta-materials are designed using Topology Optimization (TO). Tools like SIMP interpolation, mesh filtering and continuation methods are used to address the numerical issues with Topology Optimization.\u0000 In a previous research [1], by offsetting meta-material layers by a half-width of the Unit Cell, an auxetic honeycomb-like geometry was obtained. This was the first time such a shape was observed as the result of Topology Optimization targeting the effective shear modulus using square Unit Cells. This was obtained while designing the shear beam of a non-pneumatic wheel.\u0000 This research studies the design of meta-materials using offsets other than zero or half-widths. The same problem [1] was solved for different values of offset, and the obtained geometries and volume fractions are studied. It is concluded that it may be beneficial for designers to consider offsetting meta-material layers with offsets other than half-width, to design novel, potentially better performing structures.","PeriodicalId":365601,"journal":{"name":"Volume 2A: 45th Design Automation Conference","volume":"145 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131419698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the wake of increasing proliferation of renewable energy and distributed energy resources (DERs), grid designers and operators alike are faced with several emerging challenges in curbing allocative grid inefficiencies and maintaining operational stability. One such challenge relates to the increased price volatility within real-time electricity markets, a result of the inherent intermittency of renewable energy. With this challenge, however, comes heightened economic interest in exploiting the arbitrage potential of price volatility towards demand-side energy cost savings. To this end, this paper aims to maximize the arbitrage value of electricity through the optimal design of control strategies for DERs. Formulated as an arbitrage maximization problem using design optimization, and solved using reinforcement learning, the proposed approach is applied towards shared DERs within multi-building residential clusters. We demonstrate its feasibility across three unique building cluster demand profiles, observing notable energy cost reductions over baseline values. This highlights a capability for generalized learning across multiple building clusters and the ability to design efficient arbitrage policies towards energy cost minimization. Finally, the approach is shown to be computationally tractable, designing efficient strategies in approximately 5 hours of training over a simulation time horizon of 1 month.
{"title":"Designing Optimal Arbitrage Policies for Distributed Energy Systems in Building Clusters Using Reinforcement Learning","authors":"Philip Odonkor, K. Lewis","doi":"10.1115/detc2019-97190","DOIUrl":"https://doi.org/10.1115/detc2019-97190","url":null,"abstract":"\u0000 In the wake of increasing proliferation of renewable energy and distributed energy resources (DERs), grid designers and operators alike are faced with several emerging challenges in curbing allocative grid inefficiencies and maintaining operational stability. One such challenge relates to the increased price volatility within real-time electricity markets, a result of the inherent intermittency of renewable energy. With this challenge, however, comes heightened economic interest in exploiting the arbitrage potential of price volatility towards demand-side energy cost savings. To this end, this paper aims to maximize the arbitrage value of electricity through the optimal design of control strategies for DERs. Formulated as an arbitrage maximization problem using design optimization, and solved using reinforcement learning, the proposed approach is applied towards shared DERs within multi-building residential clusters. We demonstrate its feasibility across three unique building cluster demand profiles, observing notable energy cost reductions over baseline values. This highlights a capability for generalized learning across multiple building clusters and the ability to design efficient arbitrage policies towards energy cost minimization. Finally, the approach is shown to be computationally tractable, designing efficient strategies in approximately 5 hours of training over a simulation time horizon of 1 month.","PeriodicalId":365601,"journal":{"name":"Volume 2A: 45th Design Automation Conference","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131742977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Inspired by gradient structures in the nature, Gradient Nanostructured (GNS) metals have emerged as a new class of materials with tunable microstructures. GNS metals can exhibit unique combinations of material properties in terms of ultrahigh strength, good tensile ductility and enhanced strain hardening, superior fatigue and wear resistance. However, it is still challenging to fully understand the fundamental gradient structure-property relationship, which hinders the rational design of GNS metals with optimized target properties. In this paper, we developed an adaptive design framework based on simulation-based surrogate modeling to investigate how the grain size gradient and twin thickness gradient affect the strength of GNS metals. The Gaussian Process (GP) based surrogate modeling technique with adaptive sequential sampling is employed for the development of surrogate models for the gradient structure-property relationship. The proposed adaptive design integrates physics-based simulation, surrogate modeling, uncertainty quantification and optimization, which can efficiently explore the design space and identify the optimized design of GNS metals with maximum strength using limited sampling data generated from high fidelity but computational expensive physics-based simulations.
{"title":"Design of Gradient Nanotwinned Metal Materials Using Adaptive Gaussian Process Based Surrogate Models","authors":"Haofei Zhou, Xin Chen, Yumeng Li","doi":"10.1115/detc2019-97659","DOIUrl":"https://doi.org/10.1115/detc2019-97659","url":null,"abstract":"\u0000 Inspired by gradient structures in the nature, Gradient Nanostructured (GNS) metals have emerged as a new class of materials with tunable microstructures. GNS metals can exhibit unique combinations of material properties in terms of ultrahigh strength, good tensile ductility and enhanced strain hardening, superior fatigue and wear resistance. However, it is still challenging to fully understand the fundamental gradient structure-property relationship, which hinders the rational design of GNS metals with optimized target properties. In this paper, we developed an adaptive design framework based on simulation-based surrogate modeling to investigate how the grain size gradient and twin thickness gradient affect the strength of GNS metals. The Gaussian Process (GP) based surrogate modeling technique with adaptive sequential sampling is employed for the development of surrogate models for the gradient structure-property relationship. The proposed adaptive design integrates physics-based simulation, surrogate modeling, uncertainty quantification and optimization, which can efficiently explore the design space and identify the optimized design of GNS metals with maximum strength using limited sampling data generated from high fidelity but computational expensive physics-based simulations.","PeriodicalId":365601,"journal":{"name":"Volume 2A: 45th Design Automation Conference","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133809215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper we describe a new framework for planning functionally graded and multi-step fabrication processes for use in rapid prototyping applications. This framework is contributing to software tools that will simplify planning multi-material manufacturing processes and thereby make this type of manufacturing more accessible. We introduce the material description itself, low-level operations which can be used to combine one or more geometries together, and algorithms which assist the designer in computing manufacturing-compatible sequences. We then apply these tools to several example scenarios. First, we demonstrate the use of a Gaussian blur to add graded material transitions to a model which can then be produced using a multimaterial 3D printing process. Our second example highlights our solution to the problem of inserting a discrete, off-the-shelf part into a 3D printed model during the printing sequence. Finally, we implement this second example and manufacture two example components. The results show that the framework can be used to effectively generate the files needed to produce specific classes of parts.
{"title":"Voxel-Based CAD Framework for Planning Functionally Graded and Multi-Step Rapid Fabrication Processes","authors":"Cole Brauer, Daniel M. Aukes","doi":"10.1115/detc2019-98103","DOIUrl":"https://doi.org/10.1115/detc2019-98103","url":null,"abstract":"\u0000 In this paper we describe a new framework for planning functionally graded and multi-step fabrication processes for use in rapid prototyping applications. This framework is contributing to software tools that will simplify planning multi-material manufacturing processes and thereby make this type of manufacturing more accessible. We introduce the material description itself, low-level operations which can be used to combine one or more geometries together, and algorithms which assist the designer in computing manufacturing-compatible sequences. We then apply these tools to several example scenarios. First, we demonstrate the use of a Gaussian blur to add graded material transitions to a model which can then be produced using a multimaterial 3D printing process. Our second example highlights our solution to the problem of inserting a discrete, off-the-shelf part into a 3D printed model during the printing sequence. Finally, we implement this second example and manufacture two example components. The results show that the framework can be used to effectively generate the files needed to produce specific classes of parts.","PeriodicalId":365601,"journal":{"name":"Volume 2A: 45th Design Automation Conference","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115155359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
John K. Ostrander, Lauren Ryan, Snehal Dhengre, Christopher McComb, T. Simpson, N. Meisel
Virtual Reality (VR) has been shown to be an effective assistive tool in the engineering design process, aiding designers in ergonomics studies, data visualization, and manufacturing simulation. Yet there is little research exploring the advantages of VR to assist in the design for the additive manufacturing (DfAM) process. VR may present advantages over traditional computer-aided design (CAD) tools, and these advantages may be more evident as designs become more complex. The following study investigates two types of environments: 1) Immersive Virtual Reality (VR) and 2) Non-Immersive Virtual Reality (CAD) and the advantages that each environment gives to designers to assess parts for additive manufacturing. The two environments are compared to assess potential differences in DfAM decision-making. Participants familiar with DfAM are tasked with evaluating five designs of varying complexity using the Design for Additive Manufacturing Worksheet. Participant scores, evaluation times, and self-reported metrics are recorded and analyzed. Our findings indicate that as part complexity increases, DfAM scores and evaluation times increasingly differ between VR and CAD groups. We found that the VR group evaluates more complex parts at a faster rate, but with a lower accuracy when compared to the CAD group. In evaluating self-reported metrics, both groups were relatively similar; however, the CAD group reported improved confidence in identifying stress concentrations in DfAM parts. Our findings in this research identify VR as a design evaluation tool that enhances evaluation speed which speaks to its efficiency and usability; however, VR in its current form may not present the resolution necessary to identify smaller details when compared to CAD, the more accurate evaluation tool.
{"title":"A Comparative Study of Virtual Reality and Computer-Aided Design to Evaluate Parts for Additive Manufacturing","authors":"John K. Ostrander, Lauren Ryan, Snehal Dhengre, Christopher McComb, T. Simpson, N. Meisel","doi":"10.1115/detc2019-97480","DOIUrl":"https://doi.org/10.1115/detc2019-97480","url":null,"abstract":"\u0000 Virtual Reality (VR) has been shown to be an effective assistive tool in the engineering design process, aiding designers in ergonomics studies, data visualization, and manufacturing simulation. Yet there is little research exploring the advantages of VR to assist in the design for the additive manufacturing (DfAM) process. VR may present advantages over traditional computer-aided design (CAD) tools, and these advantages may be more evident as designs become more complex. The following study investigates two types of environments: 1) Immersive Virtual Reality (VR) and 2) Non-Immersive Virtual Reality (CAD) and the advantages that each environment gives to designers to assess parts for additive manufacturing. The two environments are compared to assess potential differences in DfAM decision-making. Participants familiar with DfAM are tasked with evaluating five designs of varying complexity using the Design for Additive Manufacturing Worksheet. Participant scores, evaluation times, and self-reported metrics are recorded and analyzed. Our findings indicate that as part complexity increases, DfAM scores and evaluation times increasingly differ between VR and CAD groups. We found that the VR group evaluates more complex parts at a faster rate, but with a lower accuracy when compared to the CAD group. In evaluating self-reported metrics, both groups were relatively similar; however, the CAD group reported improved confidence in identifying stress concentrations in DfAM parts. Our findings in this research identify VR as a design evaluation tool that enhances evaluation speed which speaks to its efficiency and usability; however, VR in its current form may not present the resolution necessary to identify smaller details when compared to CAD, the more accurate evaluation tool.","PeriodicalId":365601,"journal":{"name":"Volume 2A: 45th Design Automation Conference","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122164619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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